HI, I think that the first approximation generate a smaller model that require lower memory. But i don't know how much memory will consume.
I don't know if there are any way to estimate how much memory the model needs

Unread postby Manassaldi » 3 weeks ago
Hi again PeterBe, I think this can works

x(t) and c(t) are continuous variables
p(t) are binary variables
M is a sufficiently large number (BigM parameter)

if "p=1", by "eq3" (x-c) is greater than 0 so by "eq1" and "eq2" absvalue = x-c (the rest of equation are also satisfied)
if "p=0", by "eq6" (x-c) is lower than 0 so by "eq4" and "eq5" absvalue = c-x (the rest of equation are also satisfied)

Is x(tp) a binary variable. Because as far as I understood x(tp) is a continious variable whereas in the previous models (BigM) x(tp) is a binary variable. I am talking about the model that you posted some weeks ago:

Hi, x(tp) is the sum of the product between a binary variable and its position, so I suppose that is an integer variable (not binary). Anyway, I think it's better to define it as a continuous variable.

Hi, x(tp) is the sum of the product between a binary variable and its position, so I suppose that is an integer variable (not binary). Anyway, I think it's better to define it as a continuous variable.

I think I am getting confused. Let's have a look at the BigM fomulation:

So you canot thinkg about a way how to combine the inital BigM formulation with the "absoulte value" in the objective function? It ried to combine the two formulations but did not really know how to do that